In this paper, an effective solution is proposed for joint beam and power scheduling (JBPS) in the netted Colocated MIMO (C-MIMO) radar systems used for distributed multi-target tracking (MTT). At its core, the proposed solution includes a distributed fusion architecture that reduces the communication requirements while maintaining the overall robustness of the system. The distributed fusion architecture employs the covariance intersection (CI) fusion to address the unknown information correlations among radar nodes. Each C-MIMO radar node in the network can generate a time-varying number of beams with controllable transmitting power by waveform synthesis, thus is capable of accomplishing multiple tracking tasks simultaneously. To maximize the global MTT performance of the radar network, the proposed JBPS solution implements an online resource scheduling, regarding both the generated beams and the transmitted power of all radar nodes, based on the feedback of the MTT results. A scaled accuracy-based objective function is designed to quantify the global MTT performance while properly taking into account different target priorities on resource allocation. The Bayesian Cramer-Rao lower bound (BCRLB) for CI fusion rule is derived and utilized as the constituent of the objective function since it provides a lower bound on the accuracy of the target state estimates. The formulated JBPS problem is a non-convex optimization problem that when solved, determines two types of coupled parameters for the beam controlling and power allocation. Then, we propose a fast reward-based iterative descending approach to solve this problem effectively. Numerical results show that the proposed JBPS solution can deliver superior performance in terms of maximizing the overall MTT performance while possessing high flexibility on the resource allocation regarding different target priorities.

Power allocation is an essential part of the design of colocated multiple input and multiple output (C-MIMO) radar systems for multi-target tracking (MTT). A widely adopted power allocation approach is to directly minimize the amount of allocated power while satisfying the desired performance for each tracked object so as to conserve the power resources. However, this approach may not lead to a satisfactory solution when the available power is not sufficient to achieve the desired performance for all the targets. To overcome the limitations of this approach, a robust power allocation (RPA) methodology is proposed in this paper that is based on the quality of service framework. At its core, the proposed RPA method employs a task utility function that quantifies the tracking performance for different power allocations in a flexible manner. The Bayesian Cramer-Rao lower bound (BCRLB) is utilized to formulate the task utility function since it provides a lower bound on the accuracy of the target state estimates. By using a set of weights to quantify the importance of different tracked objects, the objective function of RPA is modeled as the weighted sum of task utility functions. This formulation of the RPA problem is demonstrated to be a non-convex optimization problem in general. We show that the task utility function is a unimodal function. Based on the specific structure of the task utility function, we propose an iterative parallel search algorithm to find the solution. Numerical experiments involving scenarios with both sufficient and insufficient power resources demonstrate the robustness and efficiency of the proposed strategy.

In this paper, we consider a distributed passive radar with non-cooperative illuminators of opportunity (IOs) operating on non-overlapping frequency bands and tackle the problem of direct target localization. Assuming that each passive radar receiver employs reference channels to measure the direct-path signals from the IOs and a surveillance channel to collect the target echoes corrupted by the direct-path interference (DPI), we derive the maximum likelihood estimator of the target position through the joint estimation of unknown parameters. This estimator reveals the masking effect of DPI on the target echoes and achieves joint DPI suppression and target position estimation. To reduce the large computational complexity of this estimator caused by a lack of knowledge of the IO signals, a suboptimal estimator is then devised by separately processing the observations from the reference and surveillance channels to sequentially estimate the unknown parameters. As a benchmark, we also derive the corresponding Cramér-Rao Lower Bound (CRLB) on the estimation error of the target position. Finally, an extensive numerical analysis is provided to assess the performance of the proposed suboptimal estimator in single- and multi-target scenarios, also in comparison with other estimators that ignore the DPI at the design stage and/or assume prior knowledge of the IO signals. Remarkably, the proposed estimator is robust against the DPI and can provide a localization accuracy close to the CRLB in practical operating conditions.

Hybrid active-passive radar (HAPR) can effectively reduce the radiation power of active nodes, thus improving the radar system's electromagnetic environmental friendliness and anti-interception capability. Meanwhile, active nodes in HAPR can provide cooperative transmitters with known signals for passive nodes, overcoming performance losses caused by non-cooperative illuminators of opportunity (IOs). Although the advantages of HAPR have been gradually recognized in recent years, there is currently almost no systematic solution to the joint localization problem involving active and passive nodes. To address this problem, we propose two target localization algorithms for distributed HAPRs based on two-step and, respectively, direct localization. In the two-step localization, we establish two geometric equation sets with consistent structures to handle different geometric relationships among the target, transmitters/IOs, and receivers associated with the active and passive measurements. In direct localization, we reduce the high-dimensional joint likelihood function constructed from active and passive received signals into a low-dimensional one through two dimensionality reductions, significantly reducing the complexity. These algorithms combine active and passive received signals, enhancing localization performance. We also derive the Cramér-Rao lower bound (CRLB), which provides a useful performance indicator. The results indicate that the two-step localization algorithm yields suboptimal performance, with localization errors approaching the CRLB only under specific conditions. The direct localization algorithm approaches the CRLB and outperforms systems using only active or passive nodes, thereby confirming its effectiveness and robustness.

In this paper, we propose an anti-deception jamming joint radar-to-target assignment and power scheduling (ADJ-JRAPS) strategy in distributed radar networks, to discriminate the false target and achieve multi-target tracking (MTT) under deception jamming. Specifically, the deception parameters, which play significant roles in distinguishing the physical targets from false targets, are integrated with the target state to be estimated in MTT. Based on target detection conditions under deception jamming, the posterior Cramér-Rao lower bound (PCRLB) of the augmented state is derived. Then, a discriminator, based on the derived PCRLB, is designed to calculate the false target rejection probability, which quantifies the discrimination performance of the radar system. Combined with system resource constraints, an ADJ-JRAPS problem for target discrimination and MTT under deception jamming is constructed. Due to the coupling of variables and the non-convexity of the false target rejection probability, the formulated problem exhibits non-convexity and is NP-hard. Hence, we first decompose the coupled variables and introduce a two-stage continuous convex approximate method to address this problem efficiently. Simulation experiments indicate that the presented ADJ-JRAPS has outstanding capabilities in handling false target discrimination and MTT problems under deception jamming.

This paper presents a method for the joint detection and tracking of weak targets in automotive radars using the multi-frame track-before-detect (MF-TBD) procedure. Generally, target tracking in automotive radars is challenging due to radar field of view (FOV) misalignment, nonlinear coordinate conversion, and self-positioning errors of the ego-vehicle, which are caused by platform motion. These issues significantly hinder the implementation of MF-TBD in automotive radars. To address these challenges, a new MF-TBD detection architecture is first proposed. It can adaptively adjust the detection threshold value based on the existence of moving targets within the radar FOV. Since the implementation of MF-TBD necessitates the inclusion of position, velocity, and yaw angle information of the ego-vehicle, each with varying degrees of measurement error, we further propose a multi-frame energy integration strategy for moving-platform radar and accurately derive the target energy integration path functions. The self-positioning errors of the ego-vehicle, which are usually not considered in some previous target tracking approaches, are well addressed. Numerical simulations and experimental results with real radar data demonstrate large detection and tracking gains over standard automotive radar processing in weak target environments.